Systematic Review of Identity-Centric Security in Cloud-Native CI/CD Pipelines
Bibliographic record
Abstract
As cloud-native CI/CD pipelines automate software delivery at scale, identity-centric security has become a critical concern. This paper reports a systematic literature review of 59 peer-reviewed studies that examine authentication and authorisation (AuthN/AuthZ) in CI/CD workflows. We synthesise key vulnerability classes, including token theft, privilege escalation, session hijacking, supply-chain abuse, and misaligned microservice identities. We then introduce a CI/CD-specific vulnerability taxonomy and systematically map established mechanisms such as OAuth 2.0, Kerberos, SAML, mTLS, RBAC/ABAC, XACML, API gateways, and MFA to the attack vectors they mitigate across the pipeline. Finally, we analyse emerging trends, including Zero-Trust Architecture, decentralised identity, service-mesh-based access control, and cryptographically anchored identity models that use blockchain and self-sovereign identity. The review exposes persistent gaps in configuration, observability, and runtime enforcement, as well as organisational barriers to adopting stronger identity controls. Our findings provide a structured foundation for designing trustworthy, identity-centric DevSecOps practices and highlight concrete research directions for securing access in cloud-native CI/CD environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".